NodeXL Research
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This is a presentation that describes at a high level some of the work we've been performing related to NodeXL and it's use to understand social media networks.
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NodeXL Research
- 1. Analyzing Social Media Networks with NodeXL Derek L. Hansen Maryland’s iSchool
- 3. Patterns are left behind
- 4. Research Goal Develop powerful tools, processes, and methods that dramatically lower the barriers for community managers and researchers to make sense of social media interactions.
- 16. Task: create insightful network visualization & explanation of online community they’d been studying (3 weeks with feedback from peers & instructor)
- 17. Data collection: diaries, observations, interviews, questionnaire, content analysis of assignments, in-class process recap
- 18. Data analysis: grounded theory approach
- 20. Finding an Alternate Admin
- 23. What Novices Need… Social media “network” data importers Better defined & recognized network visualization “genres” Improved layout algorithms The ability to share visualizations & analysis Basic network literacy
- 24. Research Questions (3) How can SNA be applied to different social media platforms to gain actionable insights? What network “genres” lead to actionable insights?
- 27. Mapping Corporate Email Communication Between Research Groups
- 28. Surgery Videos on YouTube
- 29. Finding Friendship Clusters in Facebook By Bernie Hogan
- 30. Finding Theorists in Lostpedia
- 31. Mapping Events with Twitter EventGraphs
- 32. Conclusion There is a pressing need to support community managers and researchers trying to make sense of social media data – especially relational data. Novices benefit from tight data/visualization coupling, example visualizations, data importers, good network layouts, and collaboration even from other novices. Researchers and practitioners could benefit from a pallet of network visualization “genres” for specific social media networks, which can be used to gain actionable insights.
- 33. The Future of Social Media Networks Networks and place Networks over time Comparing multiple networks Bi-modal, multiplex, affiliation and other non-standard networks Improved relational data spigots
- 34. Reflections for Social Media Researchers Don’t get bogged down in endless data exploration Network analysis is ideally coupled with qualitative methods Remove unnecessary elements of visualizations so it clearly tells your story Work with real-world clients, not just academically “interesting” work
- 37. Marc Smith
- 38. Dana Rotman
- 39. Elizabeth BonsignoreThe NodeXL Team
- 40. Ask me about… Alternate Reality Games (ARGs) in the service of education and design BioTracker – a mobile-based game designed to capture multimedia data about rare species for the Encyclopedia of Life Fact Check: HPV – a Facebook app that tests a novel design to disseminate info on stigmatized illnesses Using Twitter data to map the political bias of news outlet audiences Supporting local community action in Alexandria, Virginia with ACTion Alexandria
Notes de l'éditeur
- One key characteristic of technology-mediated communication is that it can (and typically does) capture detailed data on social interactions.Just like footprints left on the sand tell a story about walking a dog on the beach, our digital footprints tell stories about our online behaviors and interactions.The mass of data created by social media has the potential to usher in a golden age of social science and data-driven decision making.However, to put this social data to good use by researchers, as well as non-technical community managers and decision makers, we need usable and powerful tools that support social media data analysis.